https://github.com/justinmarschall/presentations

• Breadth vs. depth of knowledge
• You can always look something up to learn more, but you should first know that it exists
• Takeaway: file some of this talk away for future learning

• Getting Started
  • What?
  • Why?
  • How? (Installing R & RStudio)
  • Basics
• Essential R: 10 Packages for the New and Intermediate UseR
• Appendix
  • Resources to learn R
  • Publicly available data
  • R-centric academic journals
  • Specific resources for 10 essential packages

• Programming language
• Open-source project
• Encourages user contribution and package development
• Popular in statistics, research, academia, and data science

• IDE
• Open-source project
• One of many possible environments to run R code
• Highly recommended

• R is infinitely flexible
• Open source nature allows for immediate access to newest techniques/algorithms/viz/etc.

https://rmarkdown.rstudio.com/flexdashboard/examples.html

https://shiny.rstudio.com/gallery/

• Download links
  • R
  • RStudio
• R is just the language; RStudio is just the environment to run it in
• Both downloads are required
• Once installed, open RStudio

print("Hello, World!")

## [1] "Hello, World!"

?mean

str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

install.packages("tidyverse")

library(tidyverse)

devtools::install_github("hadley/tidyverse")

library(tidyverse)

count(iris, Species)

# ctrl + shift + m shortcut in RStudio
iris %>% 
  count(Species)

## # A tibble: 3 x 2
##   Species        n
##   <fct>      <int>
## 1 setosa        50
## 2 versicolor    50
## 3 virginica     50

head(iris$Sepal.Length)

## [1] 5.1 4.9 4.7 4.6 5.0 5.4

c(1, 2, 3, 4, 5)

## [1] 1 2 3 4 5

# alt + - shortcut in RStudio
x <- 1:10

x

##  [1]  1  2  3  4  5  6  7  8  9 10

str(iris)

## 'data.frame':    150 obs. of  5 variables:
##  $ Sepal.Length: num  5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...
##  $ Sepal.Width : num  3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...
##  $ Petal.Length: num  1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...
##  $ Petal.Width : num  0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...
##  $ Species     : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ...

typeof(iris$Sepal.Length)

## [1] "double"

class(iris$Sepal.Length)

## [1] "numeric"

• is.*() and as.*() functions

as.character(x)

##  [1] "1"  "2"  "3"  "4"  "5"  "6"  "7"  "8"  "9"  "10"

as.numeric(c(TRUE, FALSE))

## [1] 1 0

is.double(1.25)

## [1] TRUE

x[3] <- NA; x

##  [1]  1  2 NA  4  5  6  7  8  9 10

is.na(x)

##  [1] FALSE FALSE  TRUE FALSE FALSE FALSE FALSE FALSE FALSE FALSE

mean(is.na(x))

## [1] 0.1

mean(x, na.rm = TRUE)

## [1] 5.777778

# good code includes comments

# comment the why, not the what

# ctrl + shift + c shortcut in RStudio

fit <- aov(Sepal.Length ~ Species, data = iris)

summary(fit)

##              Df Sum Sq Mean Sq F value Pr(>F)    
## Species       2  63.21  31.606   119.3 <2e-16 ***
## Residuals   147  38.96   0.265                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

TukeyHSD(fit)

##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Sepal.Length ~ Species, data = iris)
## 
## $Species
##                       diff       lwr       upr p adj
## versicolor-setosa    0.930 0.6862273 1.1737727     0
## virginica-setosa     1.582 1.3382273 1.8257727     0
## virginica-versicolor 0.652 0.4082273 0.8957727     0

• R is case sensitive
• R lets you do dumb things (protip: don't do dumb things)

T

## [1] TRUE

T <- F

T

## [1] FALSE

• dplyr
• ggplot2
• tidyr
• broom
• purrr
• plotly
• lubridate
• stringr
• forcats
• rmarkdown

"The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures."

https://www.tidyverse.org/

library(tidyverse)

• A grammar of data manipulation
• Manipulate, transform, and summarize data quickly
• SQL-esque

library(dplyr)

• filter()
• arrange()
• select()
• rename()
• mutate()
• summarize()
• group_by()
• ntile()
• sample_n()
• inner_join()
• count()

iris %>% 
  group_by(Species) %>% 
  summarize(mean_sl = mean(Sepal.Length),
            sd_sl = sd(Sepal.Length),
            n = n())

## # A tibble: 3 x 4
##   Species    mean_sl sd_sl     n
##   <fct>        <dbl> <dbl> <int>
## 1 setosa        5.01 0.352    50
## 2 versicolor    5.94 0.516    50
## 3 virginica     6.59 0.636    50

• A graphical powerhouse, based on The Grammer of Graphics
• Produces publication worthy data visualizations
• Features built-in statistical tools
• Incredibly customizable

library(ggplot2)

• Every ggplot has 3 elements:
  • Data
  • Aesthetic mapping between variables and visuals
  • Geom to render observations
• Top-level aes() mapping inherited by downstream code
• Additional layers and customization added as desired

# basic structure
ggplot(df, aes()) +
  geom_*()

• x
• y
• alpha
• color
• fill
• shape
• size
• linetype

• geom_col()
• geom_bar()
• geom_histogram()
• geom_density()
• geom_density2d()
• geom_smooth()
• geom_point()
• geom_line()
• geom_boxplot()
• geom_raster()
• geom_text()

ggplot(diamonds, aes(x = carat, y = price)) +
  geom_point()

ggplot(diamonds, aes(x = carat, y = price, color = color)) +
  geom_point()

ggplot(diamonds, aes(x = carat, y = price, color = color)) +
  geom_point() +
  facet_wrap(~cut)

ggplot(diamonds, aes(x = carat, y = price, color = color)) +
  geom_smooth() +
  facet_wrap(~cut)

ggplot(diamonds, aes(x = carat, y = price, color = color)) +
  geom_point() +
  facet_grid(clarity~cut) +
  labs(x = "Carat", 
       y = "Price ($)", 
       color = "Color", 
       title = "Diamond Price by the 4 C's", 
       subtitle = "Carat, Color, Cut, & Clarity") +
  theme_minimal()

• Tidy, clean, reshape, & prepare data
• Tidy Data (Wickham, 2014)
  • Each variable forms a column
  • Each observation forms a row
  • Each type of observational unit forms a table

library(tidyr)

• gather()
• spread()
• nest()
• separate()
• replace_na()

tibble(norm_1 = rnorm(1000, mean = 0, sd = 1),
       norm_2 = rnorm(1000, mean = 0, sd = 2),
       norm_3 = rnorm(1000, mean = 0, sd = 3))

## # A tibble: 1,000 x 3
##     norm_1  norm_2 norm_3
##      <dbl>   <dbl>  <dbl>
##  1  1.33    1.56   -2.12 
##  2 -0.964  -0.543   0.993
##  3  0.540   0.193   1.88 
##  4  0.493  -1.79   -0.773
##  5 -0.0258 -4.58    3.32 
##  6  1.04    0.880   5.48 
##  7  0.305  -2.98   -2.46 
##  8  0.432   0.874  -1.69 
##  9  0.480   1.24    0.125
## 10  0.390   0.0408  1.99 
## # ... with 990 more rows

tibble(norm_1 = rnorm(1000, mean = 0, sd = 1),
       norm_2 = rnorm(1000, mean = 0, sd = 2),
       norm_3 = rnorm(1000, mean = 0, sd = 3)) %>% 
  gather(dist, value)

## # A tibble: 3,000 x 2
##    dist      value
##    <chr>     <dbl>
##  1 norm_1 -0.0632 
##  2 norm_1 -0.487  
##  3 norm_1  1.48   
##  4 norm_1 -0.673  
##  5 norm_1  0.724  
##  6 norm_1  0.911  
##  7 norm_1 -0.00545
##  8 norm_1  0.739  
##  9 norm_1 -0.0564 
## 10 norm_1  1.87   
## # ... with 2,990 more rows

tibble(norm_1 = rnorm(1000, mean = 0, sd = 1),
       norm_2 = rnorm(1000, mean = 0, sd = 2),
       norm_3 = rnorm(1000, mean = 0, sd = 3)) %>% 
  gather(dist, value) %>% 
  ggplot(aes(x = value, fill = dist)) +
    geom_density(alpha = 0.6) +
    labs(x = "Value", y = "Density", fill = "Distribution") +
    scale_x_continuous(breaks = seq(-20, 20, 2.5)) +
    scale_fill_viridis_d() +
    theme_minimal()

• Provides summary statistical information in easy to use tables

library(broom)

• tidy()
• glance()
• augment()

fit <- lm(price ~ carat, data = diamonds)

summary(fit)

## 
## Call:
## lm(formula = price ~ carat, data = diamonds)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18585.3   -804.8    -18.9    537.4  12731.7 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept) -2256.36      13.06  -172.8   <2e-16 ***
## carat        7756.43      14.07   551.4   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1549 on 53938 degrees of freedom
## Multiple R-squared:  0.8493, Adjusted R-squared:  0.8493 
## F-statistic: 3.041e+05 on 1 and 53938 DF,  p-value: < 2.2e-16

tidy(fit)

## # A tibble: 2 x 5
##   term        estimate std.error statistic p.value
##   <chr>          <dbl>     <dbl>     <dbl>   <dbl>
## 1 (Intercept)   -2256.      13.1     -173.       0
## 2 carat          7756.      14.1      551.       0

glance(fit)

## # A tibble: 1 x 11
##   r.squared adj.r.squared sigma statistic p.value    df  logLik    AIC
##       <dbl>         <dbl> <dbl>     <dbl>   <dbl> <int>   <dbl>  <dbl>
## 1     0.849         0.849 1549.   304051.       0     2 -4.73e5 9.45e5
## # ... with 3 more variables: BIC <dbl>, deviance <dbl>, df.residual <int>

augment(fit)

## # A tibble: 53,940 x 9
##    price carat .fitted .se.fit .resid      .hat .sigma   .cooksd .std.resid
##    <int> <dbl>   <dbl>   <dbl>  <dbl>     <dbl>  <dbl>     <dbl>      <dbl>
##  1   326 0.23  -472.     10.4    798. 0.0000452  1549.   6.00e-6      0.516
##  2   326 0.21  -628.     10.6    954. 0.0000471  1549.   8.92e-6      0.616
##  3   327 0.23  -472.     10.4    799. 0.0000452  1549.   6.02e-6      0.516
##  4   334 0.290   -7.00    9.77   341. 0.0000398  1549.   9.66e-7      0.220
##  5   335 0.31   148.      9.57   187. 0.0000382  1549.   2.78e-7      0.121
##  6   336 0.24  -395.     10.3    731. 0.0000442  1549.   4.93e-6      0.472
##  7   336 0.24  -395.     10.3    731. 0.0000442  1549.   4.93e-6      0.472
##  8   337 0.26  -240.     10.1    577. 0.0000424  1549.   2.94e-6      0.372
##  9   337 0.22  -550.     10.5    887. 0.0000461  1549.   7.56e-6      0.573
## 10   338 0.23  -472.     10.4    810. 0.0000452  1549.   6.18e-6      0.523
## # ... with 53,930 more rows

• Functional programming toolkit
• Dramatically improves iteration process
• Can often replace for() loops with better performance and increased transparency in code
• More predictable output than base R's apply() family of functions

• map()
• map2()
• pmap()

# imagine data.frame object d already exists and we're creating new, random variables
d$var1  <- rnorm(n = 1000, mean = 1)
d$var2  <- rnorm(n = 1000, mean = 2)
d$var3  <- rnorm(n = 1000, mean = 3)
d$var3  <- rnorm(n = 1000, mean = 4)
d$var5  <- rnorm(n = 1000, mean = 5)
d$var6  <- rnorm(n = 1000, mean = 6)
d$var7  <- rnorm(n = 1000, mean = 7)
d$var8  <- rnorm(n = 1000, mean = 8)
d$var9  <- rnorm(n = 1000, mean = 9)
d$var10 <- rnorm(n = 1000, mean = 10)

• Did you catch the typo?

map_dfc(1:10, rnorm, n = 1000)

## # A tibble: 1,000 x 10
##         V1    V2    V3    V4    V5    V6    V7    V8    V9   V10
##      <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
##  1  2.44   0.714  3.00  2.61  3.50  5.35  7.06  7.66  8.31  9.98
##  2 -0.333  2.55   3.81  4.89  4.99  4.44  7.22  5.51  9.79  9.71
##  3  1.36   2.32   2.77  4.11  5.93  5.38  8.69  7.81  6.67  9.32
##  4  0.854  3.96   1.95  4.12  6.05  6.21  6.30  8.24  8.40  8.27
##  5 -0.0318 1.89   1.14  3.58  5.13  4.08  8.02  7.45 10.4   8.52
##  6  2.80   1.76   2.91  3.06  5.16  7.38  5.78  7.96  8.79 11.3 
##  7  0.141  1.62   2.76  4.86  4.45  5.95  6.53  8.07 10.1  11.0 
##  8  1.63   1.02   3.65  3.58  3.91  4.96  7.17  8.31  9.18  9.77
##  9  0.609  3.01   3.17  4.76  6.48  6.88  6.86  7.41 10.0  10.6 
## 10  0.517  2.34   3.69  4.58  3.86  5.93  7.01  6.32  9.11  8.45
## # ... with 990 more rows

• Create interactive visualizations that render in HTML
• Great integration with rmarkdown and shiny
• Impress your friends

library(plotly)

• ggplotly()
• plot_ly()
• add_trace()
• layout()

• Build your plot in ggplot2 and wrap it in a ggplotly() function
• Might have some limitations, but will cover most use-cases

p <- 
  ggplot(diamonds, aes(x = cut, y = price)) + 
    geom_boxplot()

ggplotly(p)

plot_ly(iris, x = ~Sepal.Length, y = ~Sepal.Width, 
        text = ~Species, color = ~Species, colors = "Set1") %>% 
  add_trace(type = 'scatter', mode = 'markers') %>% 
  layout(xaxis = list(title = "Sepal Length"),
         yaxis = list(title = "Sepal Width"),
         title = "Iris Data")

library(gapminder)

gapminder %>%
  plot_ly(
    x = ~gdpPercap, 
    y = ~lifeExp, 
    size = ~pop, 
    color = ~continent, 
    colors = "Set1",
    frame = ~year, 
    text = ~country, 
    hoverinfo = "text",
    type = "scatter",
    mode = "markers"
  ) %>%
  layout(
    xaxis = list(
      type = "log"
    )
  )

• Makes working with dates in R very smooth
• Package good at…
  • Parsing date-time variables
  • Subsetting dates
  • Date math and manipulation

library(lubridate)

• Convert strings or numbers to date-time
• Multiple functions to aid with this, depending on information and ordering

ymd(20190412)

## [1] "2019-04-12"

mdy("April 12th, 2019")

## [1] "2019-04-12"

• Again, multiple functions–these are just a few

now()

## [1] "2019-04-10 15:28:32 CDT"

year(now())

## [1] 2019

month(now(), label = TRUE, abbr = FALSE)

## [1] April
## 12 Levels: January < February < March < April < May < June < ... < December

ymd(20190330) - months(1)

## [1] NA

ymd(20190330) %m-% months(1)

## [1] "2019-02-28"

• String manipulation for the tidyverse
• A more predictable and transparent API than base R's grep() family of functions

library(stringr)

• Detect matches
• Mutate strings
• Subset strings
• Join and split
• Manage lengths
• Order strings
• Regular expressions

names(iris)

## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 
## [5] "Species"

iris %>% 
  as_tibble() %>% 
  rename_all(funs(str_to_lower(str_replace_all(., "\\.", "_")))) %>% 
  head()

## # A tibble: 6 x 5
##   sepal_length sepal_width petal_length petal_width species
##          <dbl>       <dbl>        <dbl>       <dbl> <fct>  
## 1          5.1         3.5          1.4         0.2 setosa 
## 2          4.9         3            1.4         0.2 setosa 
## 3          4.7         3.2          1.3         0.2 setosa 
## 4          4.6         3.1          1.5         0.2 setosa 
## 5          5           3.6          1.4         0.2 setosa 
## 6          5.4         3.9          1.7         0.4 setosa

• Functions for categorical data (factors)

library(forcats)

• Inspect factors
• Combine factors
• Change the order of levels
• Change the value of levels
• Add or drop levels

diamonds %>% 
  count(cut) %>% 
  mutate(cut = fct_reorder(cut, n)) %>% 
  ggplot(aes(x = cut, y = n)) +
    geom_col() +
    coord_flip()

• Produce "…high quality documents, reports, presentations and dashboards"
• Excels in reproducible, interactive, parameterized work
• Great for teaching and sharing code

https://rmarkdown.rstudio.com/

library(rmarkdown)

names(knitr::knit_engines$get())

##  [1] "awk"       "bash"      "coffee"    "gawk"      "groovy"   
##  [6] "haskell"   "lein"      "mysql"     "node"      "octave"   
## [11] "perl"      "psql"      "Rscript"   "ruby"      "sas"      
## [16] "scala"     "sed"       "sh"        "stata"     "zsh"      
## [21] "highlight" "Rcpp"      "tikz"      "dot"       "c"        
## [26] "fortran"   "fortran95" "asy"       "cat"       "asis"     
## [31] "stan"      "block"     "block2"    "js"        "css"      
## [36] "sql"       "go"        "python"    "julia"

https://bookdown.org/yihui/rmarkdown/language-engines.html

• HTML
• PDF
• Microsoft Word
• OpenDocument Text
• Rich Text Format
• Markdown
• Github compatible markdown
• ioslides HTML slides (the format of this presentation!)
• slidy HTML slides
• Beamer pdf slides

• shiny and shinydashboard
• gganimate
• flexdashboard
• zoo
• psych
• readr
• readxl
• haven
• devtools
• recipes
• caret
• parsnip
• gt

To apply or learn more visit www.colonybrands.com

justin.marschall@imsdm.com
jcmarschall
justinmarschall
justinmarschall

https://github.com/justinmarschall/presentations

• R for Data Science
• Advanced R
• The Tidyverse Cookbook
• R Graphics Cookbook
• Learning Statistics with R
• The tidyverse style guide
• Applied Predictive Modeling

• RStudio Cheat Sheets
• RStudio Online Learning
• RStudio Webinars
• Codecademy
• R Vocabulary
• swirl
• Happy Git and GitHub for the useR

• UCI Machine Learning Repository
• Kaggle
• Sports Reference
• Quandl
• data.gov
• FiveThirtyEight
• Built-in data sets in R

data()

• The R Journal
• Journal of Statistical Software

• dplyr cheat sheet
• Introduction to dplyr
• Two-table verbs
• Window functions

• ggplot2 cheat sheet
• Aesthetic specifications
• tidyverse: ggplot2
• ggplot2 extensions

• tidyr cheat sheet (see pg. 2)
• Tidy data
• Introducing tidyr

• Introduction to broom
• Solving the model representation problem with broom

• purrr cheat sheet
• purrr tutorial
• Intro to purrr
• purrr cookbook

• plotly cheat sheet
• plotly R Open Source Graphing Library
• plotly for R

• lubridate cheat sheet
• Introduction to lubridate

• stringr cheat sheet
• Introduction to stringr

• forcats cheat sheet
• Introduction to forcats

• R Markdown cheat sheet
• R Markdown home page
• R Markdown: The Definitive Guide

• https://stackoverflow.blog/2017/10/10/impressive-growth-r/
• http://r4stats.com/articles/popularity/

• R Logo
  • CC-BY-SA 4.0 License; Modified to be square
  • https://www.r-project.org/logo/